import argparse
import datetime
import json
import os
import random
import time
from pathlib import Path

import numpy as np
import ruamel.yaml as yaml
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist

import utils
from data import create_dataset, create_sampler, create_loader
from models.blip_nlvr import blip_nlvr
from utils import cosine_lr_schedule


def train(model, data_loader, optimizer, epoch, device, config):
    # train
    model.train()

    metric_logger = utils.MetricLogger(delimiter="  ")
    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=50, fmt='{value:.6f}'))
    metric_logger.add_meter('loss', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))

    header = 'Train Epoch: [{}]'.format(epoch)
    print_freq = 50
    step_size = 10

    for i, (image0, image1, text, targets) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
        images = torch.cat([image0, image1], dim=0)
        images, targets = images.to(device), targets.to(device)

        loss = model(images, text, targets=targets, train=True)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        metric_logger.update(lr=optimizer.param_groups[0]["lr"])
        metric_logger.update(loss=loss.item())

        # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    print("Averaged stats:", metric_logger.global_avg())
    return {k: "{:.4f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}


@torch.no_grad()
def evaluate(model, data_loader, device, config):
    # test
    model.eval()

    metric_logger = utils.MetricLogger(delimiter="  ")

    header = 'Evaluation:'
    print_freq = 50

    for image0, image1, text, targets in metric_logger.log_every(data_loader, print_freq, header):
        images = torch.cat([image0, image1], dim=0)
        images, targets = images.to(device), targets.to(device)

        prediction = model(images, text, targets=targets, train=False)

        _, pred_class = prediction.max(1)
        accuracy = (targets == pred_class).sum() / targets.size(0)

        metric_logger.meters['acc'].update(accuracy.item(), n=image0.size(0))

    # gather the stats from all processes
    metric_logger.synchronize_between_processes()

    print("Averaged stats:", metric_logger.global_avg())
    return {k: "{:.4f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}


def main(args, config):
    utils.init_distributed_mode(args)

    device = torch.device(args.device)

    # fix the seed for reproducibility
    seed = args.seed + utils.get_rank()
    torch.manual_seed(seed)
    np.random.seed(seed)
    random.seed(seed)
    cudnn.benchmark = True

    #### Dataset #### 
    print("Creating dataset")
    datasets = create_dataset('nlvr', config)

    if args.distributed:
        num_tasks = utils.get_world_size()
        global_rank = utils.get_rank()
        samplers = create_sampler(datasets, [True, False, False], num_tasks, global_rank)
    else:
        samplers = [None, None, None]

    batch_size = [config['batch_size_train'], config['batch_size_test'], config['batch_size_test']]
    train_loader, val_loader, test_loader = create_loader(datasets, samplers, batch_size=batch_size,
                                                          num_workers=[4, 4, 4], is_trains=[True, False, False],
                                                          collate_fns=[None, None, None])

    #### Model #### 
    print("Creating model")
    model = blip_nlvr(pretrained=config['pretrained'], image_size=config['image_size'],
                      vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'])

    model = model.to(device)

    model_without_ddp = model
    if args.distributed:
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
        model_without_ddp = model.module

    optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay'])

    print("Start training")
    start_time = time.time()
    best = 0
    best_epoch = 0

    for epoch in range(0, config['max_epoch']):
        if not args.evaluate:
            if args.distributed:
                train_loader.sampler.set_epoch(epoch)

            cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr'])

            train_stats = train(model, train_loader, optimizer, epoch, device, config)

        val_stats = evaluate(model, val_loader, device, config)
        test_stats = evaluate(model, test_loader, device, config)

        if utils.is_main_process():
            if args.evaluate:
                log_stats = {**{f'val_{k}': v for k, v in val_stats.items()},
                             **{f'test_{k}': v for k, v in test_stats.items()},
                             }
                with open(os.path.join(args.output_dir, "log.txt"), "a") as f:
                    f.write(json.dumps(log_stats) + "\n")

            else:
                log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
                             **{f'val_{k}': v for k, v in val_stats.items()},
                             **{f'test_{k}': v for k, v in test_stats.items()},
                             'epoch': epoch,
                             }

                if float(val_stats['acc']) > best:
                    save_obj = {
                        'model': model_without_ddp.state_dict(),
                        'optimizer': optimizer.state_dict(),
                        'config': config,
                        'epoch': epoch,
                    }
                    torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_best.pth'))
                    best = float(val_stats['acc'])
                    best_epoch = epoch

                with open(os.path.join(args.output_dir, "log.txt"), "a") as f:
                    f.write(json.dumps(log_stats) + "\n")
        if args.evaluate:
            break

        dist.barrier()

    if utils.is_main_process():
        with open(os.path.join(args.output_dir, "log.txt"), "a") as f:
            f.write("best epoch: %d" % best_epoch)

    total_time = time.time() - start_time
    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
    print('Training time {}'.format(total_time_str))


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--config', default='./configs/nlvr.yaml')
    parser.add_argument('--output_dir', default='output/NLVR')
    parser.add_argument('--evaluate', action='store_true')
    parser.add_argument('--device', default='cuda')
    parser.add_argument('--seed', default=42, type=int)
    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')
    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
    parser.add_argument('--distributed', default=True, type=bool)
    args = parser.parse_args()

    config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)

    Path(args.output_dir).mkdir(parents=True, exist_ok=True)

    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))

    main(args, config)
